Hypothesis-Conditioned Query Rewriting for Decision-Useful Retrieval

ArXi:2603.19008v1 Announce Type: cross Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by grounding generation in external, non-parametric knowledge. However, when a task requires choosing among competing options, simply grounding generation in broadly relevant context is often insufficient to drive the final decision. Existing RAG methods typically rely on a single initial query, which often favors topical relevance over decision-relevant evidence, and. therefore. retrieves background information that can fail to discriminate among answer options.